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\section{Introduction}
Predicting links between nodes in social networks merits relevance in business
and security. Discovering links between members gives insight about
collaborations and interactions between individuals that ultimately aids in
making decisions or recommendations.

As explained by Elkan in \cite{class_notes}, we can learn about links
using both supervised and unsupervised learning techniques to predict both
existence and types of links.

However, unsupervised learning methods is a large field, so we will focus on
exploring supervised learning techniques to construct binary classifiers for
predicting links and their type in the modified Profiles for Terror
dataset \cite{pitlink}. 

For this assignment, we use the Rapidminer SVD operator for computing singular
value decomposition of a matrix, Fast Large Margin operator with
L2 Logistic Regression kernel for binary classification, and R extensions 
to perform various matrix operations such as computing the
adjacency matrix and pointwise multiplication of feature vectors.

The remainder of this report is organized as follows. 
Section \ref{sec:dataset} details the preprocessing techniques we investigate.
Section \ref{sec:experiments} explains the various techniques we try.
Section \ref{sec:results} presents the results of our experiments.
Section \ref{sec:discussion} discusses our models.
Section \ref{sec:conclusion} concludes. 
